research-article

OJXPerf: featherlight object replica detection for Java programs

Authors:

Shuyin Jiao, and

Xu LiuAuthors Info & Claims

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

May 2022

Pages 1558 - 1570

https://doi.org/10.1145/3510003.3510083

Published: 05 July 2022 Publication History

Abstract

Memory bloat is an important source of inefficiency in complex production software, especially in software written in managed languages such as Java. Prior approaches to this problem have focused on identifying objects that outlive their life span. Few studies have, however, looked into whether and to what extent myriad objects of the same type are identical. A quantitative assessment of identical objects with code-level attribution can assist developers in refactoring code to eliminate object bloat, and favor reuse of existing object(s). The result is reduced memory pressure, reduced allocation and garbage collection, enhanced data locality, and reduced re-computation, all of which result in superior performance.

We develop OJXPerf, a lightweight sampling-based profiler, which probabilistically identifies identical objects. OJXPerf employs hardware performance monitoring units (PMU) in conjunction with hardware debug registers to sample and compare field values of different objects of the same type allocated at the same calling context but potentially accessed at different program points. The result is a lightweight measurement --- a combination of object allocation contexts and usage contexts ordered by duplication frequency. This class of duplicated objects is relatively easier to optimize. OJXPerf incurs 9% runtime and 6% memory overheads on average. We empirically show the benefit of OJXPerf by using its profiles to instruct us to optimize a number of Java programs, including well-known benchmarks and real-world applications. The results show a noticeable reduction in memory usage (up to 11%) and a significant speedup (up to 25%).

References

[1]

2013. Obtain object address. https://jrebel.com/rebellabs/dangerous-code-how-to-be-unsafe-with-java-classes-o\bjects-in-memory/4/.

[2]

2018. DaCapo Benchmark Suite 9.12. https://sourceforge.net/projects/dacapobench/files/9.12-bach-MR1/.

[3]

2018. OProfile. http://oprofile.sourceforge.net.

[4]

2020. AlphaGo Zero. https://deepmind.com/blog/article/alphago-zero-starting-scratch.

[5]

Ali-Reza Adl-Tabatabai, Richard L. Hudson, Mauricio J. Serrano, and Sreenivas Subramoney. 2004. Prefetch injection based on hardware monitoring and object metadata. SIGPLAN Not. 39, 6, 267--276.

Digital Library

[6]

Randy Allen and Ken Kennedy. 2001. Optimizing Compilers for Modern Architectures: A Dependence-based Approach. Morgan Kaufmann.

[7]

Matthew Arnold and Peter F. Sweeney. 1999. Approximating the Calling Context Tree via Sampling. Technical Report 21789. IBM.

[8]

RoaringBitmap authors. 2019. RoaringBitmap. https://github.com/RoaringBitmap/RoaringBitmap.

[9]

Mark Bull, Lorna Smith, Martin Westhead, David Henty, and Robert Davey. 2001. Java Grande benchmark suite. https://www.epcc.ed.ac.uk/research/computing/performance-characterisation-and-benchmarking/java-grande-benchmark-suite.

[10]

Wen-ke Chen, Sanjay Bhansali, Trishul Chilimbi, Xiaofeng Gao, and Weihaw Chuang. 2006. Profile-guided Proactive Garbage Collection for Locality Optimization. SIGPLAN Not. 41, 6, 332--340.

Digital Library

[11]

Oracle Corp. 2017. Oracle Developer Studio Performance Analyzer. https://www.oracle.com/technetwork/server-storage/solarisstudio/documentation/o11-151-perf-analyzer-brief-1405338.pdf.

[12]

Oracle Corporation. 2011. jmap Memory Map. https://docs.oracle.com/javase/7/docs/technotes/tools/share/jmap.html.

[13]

Oracle Corporation. 2018. All-in-One Java Troubleshooting Tool. https://visualvm.github.io.

[14]

Standard Performance Evaluation Corporation. 2008. SPECjvm2008 benchmark suite. https://www.spec.org/jvm2008.

[15]

Luca Della Toffola, Michael Pradel, and Thomas R. Gross. 2015. Performance Problems You Can Fix: A Dynamic Analysis of Memoization Opportunities. Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA 2015), 607--622.

Digital Library

[16]

NASA Advanced Supercomputing Division. 2018. NAS Parallel Benchmarks. https://www.nas.nasa.gov/publications/npb.html.

[17]

R. E. McLear, D. M. Scheibelhut, and E. Tammaru. 1982. Guidelines for creating a debuggable processor. Proceedings of the first international symposium on Architectural support for programming languages and operating systems (ASPLOS), 100--106.

[18]

Ariel Eizenberg, Shiliang Hu, Gilles Pokam, and Joseph Devietti. 2016. Remix: Online Detection and Repair of Cache Contention for the JVM. In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation (Santa Barbara, CA, USA) (PLDI '16). ACM, New York, NY, USA, 251--265.

Digital Library

[19]

ej-technologies GmbH. 2018. THE AWARD-WINNING ALL-IN-ONE JAVA PROFILER. https://www.ej-technologies.com/products/jprofiler/overview.html.

[20]

Peter F. Sweeney, Matthias Hauswirth, Brendon Cahoon, Perry Cheng, Amer Diwan, David Grove, and Michael Hind. 2004. Using hardware performance monitors to understand the behavior of Java applications. In Proceedings of the 3rd Virtual Machine Research and Technology Symposium (VM'04).

Digital Library

[21]

Lu Fang, Liang Dou, and Guoqing Xu. 2015. PerfBlower: Quickly Detecting Memory-Related Performance Problems via Amplification. In 29th European Conference on Object-Oriented Programming (ECOOP 2015) (Leibniz International Proceedings in Informatics (LIPIcs), Vol. 37), John Tang Boyland (Ed.). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 296--320.

[22]

Apache Software Foundation. 2017. Apache SAMOA: Scalable Advanced Massive Online Analysis. https://samoa.incubator.apache.org.

[23]

Andy Georges, Dries Buytaert, Lieven Eeckhout, and Koen De Bosschere. 2004. Method-Level Phase Behavior in Java Workloads. Proc. of the ACM SIGPLAN Conf. on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), 270--287.

Digital Library

[24]

YourKit GmbH. 2018. The Industry Leader in .NET and Java Profiling. https://www.yourkit.com.

[25]

Matthias Hauswirth, Peter F. Sweeney, Amer Diwan, and Michael Hind. 2004. Vertical Profiling: Understanding the Behavior of Object-Oriented Applications. Proc. of Conf. on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), 251--269.

Digital Library

[26]

Matthew Hertz, Stephen M. Blackburn, J. Eliot B. Moss, Kathryn S. McKinley, and Darko Stefanović. 2006. Generating object lifetime traces with Merlin. ACM Transactions on Programming Languages and Systems.

[27]

Xianglong Huang, Stephen M. Blackburn, Kathryn S. McKinley, J Eliot B. Moss, Zhenlin Wang, and Perry Cheng. 2004. The Garbage Collection Advantage: Improving Program Locality. SIGPLAN Not. 39, 10, 69--80.

Digital Library

[28]

Skymind Inc. 2019. Deep Learning for Java. https://deeplearning4j.org.

[29]

Twitter Inc. 2019. Parquet MR. https://github.com/apache/parquet-mr.

[30]

Intel Corporation. 2010. Intel 64 and IA-32 Architectures Software Developer's Manual, Volume 3B: System Programming Guide, Part 2, Number 253669-032.

[31]

Nguyen Khanh and Guoqing Xu. 2013. Cachetor: Detecting Cacheable Data to Remove Bloat. ESEC/FSE 2013 Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, 268--278.

[32]

Andreas Kull. 2020. Awesome Java. https://github.com/akullpp/awesome-java.

[33]

Jeremy Lau, Matthew Arnold, Michael Hind, and Brad Calder. 2006. Online performance auditing: Using hot optimizations without getting burned. In Proc. Conf. on Programming Language Design and Implementation (PLDI 2006), New York, USA, 239--251.

Digital Library

[34]

Linux. 2015. Linux Perf Tool. http://www.brendangregg.com/perf.html.

[35]

Stephen M. Blackburn, Robin Garner, Chris Hoffmann, Asjad M. Khang, Kathryn S. McKinley, Rotem Bentzur, Amer Diwan, Daniel Feinberg, Daniel Frampton, Samuel Z. Guyer, Martin Hirzel, Antony Hosking, Maria Jump, Han Lee, J. Eliot B. Moss, Aashish Phansalkar, Darko Stefanović, Thomas VanDrunen, Daniel von Dincklage, and Ben Wiedermann. 2006. The DaCapo benchmarks: java benchmarking development and analysis. OOPSLA '06 Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications, Portland, Oregon, USA, 169--190.

Digital Library

[36]

Trishul M. Chilimbi and James R. Larus. 1998. Using Generational Garbage Collection to Implement Cache-conscious Data Placement. SIGPLAN Not. 34, 3, 37--48.

Digital Library

[37]

Darko Marinov and Robery O'Callahan. 2003. Object Equality Profiling. ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), 313--325.

[38]

Khanh Nguyen and Guoqing Xu. 2013. Cachetor: Detecting Cacheable Data to Remove Bloat. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering (Saint Petersburg, Russia) (ESEC/FSE 2013). Association for Computing Machinery, New York, NY, USA, 268--278.

Digital Library

[39]

Adrian Nistor, Linhai Song, Darko Marinov, and Shan Lu. 2013. Toddler: Detecting Performance Problems via Similar Memory-access Patterns. In Proceedings of the 2013 International Conference on Software Engineering (San Francisco, CA, USA) (ICSE '13). IEEE Press, Piscataway, NJ, USA, 562--571.

Digital Library

[40]

Nitsan Wakart. 2016. The Pros and Cons of AsyncGetCallTrace Profilers. http://psy-lob-saw.blogspot.com/2016/06/the-pros-and-cons-of-agct.html.

[41]

Sable Research Group of McGill University. 2019. Soot. https://sable.github.io/soot/.

[42]

Oracle Corp. 2016. NetBeans profiler. https://profler.netbeans.org.

[43]

Andrei Pangin. 2018. Async-profiler. https://github.com/jvm-profiling-tools/async-profiler.

[44]

Aleksandar Prokopec, Andrea Rosà, David Leopoldseder, Gilles Duboscq, Petr Tůma, Martin Studener, Lubomír Bulej, Yudi Zheng, Alex Villazón, Doug Simon, Thomas Würthinger, and Walter Binder. 2019. Renaissance: Benchmarking Suite for Parallel Applications on the JVM. In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation (Phoenix, AZ, USA) (PLDI 2019). ACM, New York, NY, USA, 31--47.

Digital Library

[45]

Bill Pugh, Andrey Loskutov, and Keith Lea. 2015. FindBugs. http://findbugs.sourceforge.net.

[46]

Jeffrey S. Vitter. 1985. Random Sampling with a Reservoir. ACM Transactions on Mathematical Software (TOMS), 37--57.

[47]

Mark Scott Johnson. 1982. Some requirements for architectural support of software debugging. Proceedings of the first international symposium on Architectural support for programming languages and operating systems (ASPLOS), 140--148.

Digital Library

[48]

Ajeet Shankar, Mattew Arnold, and Rastislav Bodik. 2008. JOLT: Lightweight Dynamic Analysis and Removal of Object Churn. ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), 127--142.

Digital Library

[49]

Yefim Shuf, Manish Gupta, Hubertus Franke, Andrew Appel, and Jaswinder Pal Singh. 2002. Creating and Preserving Locality of Java Applications at Allocation and Garbage Collection Times. SIGPLAN Not. 37, 11, 13--25.

Digital Library

[50]

Linhai Song and Shan Lu. 2017. Performance Diagnosis for Inefficient Loops. In Proceedings of the 39th International Conference on Software Engineering (Buenos Aires, Argentina) (ICSE '17). IEEE Press, Piscataway, NJ, USA, 370--380.

Digital Library

[51]

Pengfei Su, Qingsen Wang, Chabbi Milind, and Xu Liu. 2019. Pinpointing Performance Inefficiencies in Java. The 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Tallinn, Estonia.

[52]

Nathan R. Tallent. 2010. Performance analysis for parallel programs from multicore to petascale. Ph.D. thesis. Department of Computer Science, Rice University.

[53]

Qingsen Wang, Xu Liu, and Milind Chabbi. 2019. Featherlight Reuse-Distance Measurement. Proceedings of The 25th IEEE International Symposium on High Performance Computer Architecture, 440--453.

[54]

Shasha Wen, Milind Chabbi, and Xu Liu. 2017. REDSPY: Exploring Value Locality in Software. In Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems (Xi'an, China) (ASPLOS '17). Association for Computing Machinery, New York, NY, USA, 47--61.

Digital Library

[55]

Shasha Wen, Xu Liu, John Byrne, and Milind Chabbi. 2018. Watching for Software Inefficiencies with Witch. Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '18), 440--453.

Digital Library

[56]

Guoqing Xu. 2012. Finding reusable data structures. ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), 1017--1034.

Digital Library

[57]

Guoqing Xu. 2013. Resurrector: A Tunable Object Lifetime Profiling Technique for Optimizing Real-World Programs. In Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages and Applications (Indianapolis, Indiana, USA) (OOPSLA '13). Association for Computing Machinery, New York, NY, USA, 111--130.

Digital Library

[58]

Guoqing Xu, Matthew Arnold, and Nick Mitchell. 2009. Go with the Flow: Profiling Copies To Find Runtime Bloat. Proceedings of the 30th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI '09), 419--430.

Digital Library

[59]

Guoqing Xu, Matthew Arnold, Nick Mitchell, Atanas Rountev, and Gary Sevitsky. 2009. Go with the Flow: Profiling Copies to Find Runtime Bloat. In Proceedings of the 30th ACM SIGPLAN Conference on Programming Language Design and Implementation (Dublin, Ireland) (PLDI '09). Association for Computing Machinery, New York, NY, USA, 419--430.

Digital Library

[60]

Guoqing Xu, Michael D. Bond, Feng Qin, and Atanas Rountev. 2011. LeakChaser: Helping Programmers Narrow down Causes of Memory Leaks. In Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation (San Jose, California, USA) (PLDI '11). Association for Computing Machinery, New York, NY, USA, 270--282.

Digital Library

[61]

Guoqing Xu, Nick Mitchell, Matthew Arnold, Atanas Rountev, Edith Schonberg, and Gary Sevitsky. 2014. Scalable Runtime Bloat Detection Using Abstract Dynamic Slicing. ACM Trans. Softw. Eng. Methodol. 23, 3, Article 23 (June 2014), 50 pages.

Digital Library

[62]

Guoqing Xu and Atanas Rountev. 2010. Detecting Inefficiently-Used Containers to Avoid Bloat. In Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation (Toronto, Ontario, Canada) (PLDI '10). Association for Computing Machinery, New York, NY, USA, 160--173.

Digital Library

[63]

Guoqing Xu and Atanas Rountev. 2010. Detecting Inefficiently-Used Containers to Avoid Bloat. Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI '10)., 160--173.

Digital Library

[64]

Guoqing Xu, Dacong Yan, and Atanas Rountev. 2012. Static Detection of Loop-Invariant Data Structures. In Proceedings of the 26th European Conference on Object-Oriented Programming (Beijing, China) (ECOOP'12). Springer-Verlag, Berlin, Heidelberg, 738--763.

Digital Library

[65]

Dacong Yan, Guoqing (Harry) Xu, Shengqian Yang, and Atanas Rountev. 2014. LeakChecker: Practical Static Memory Leak Detection for Managed Languages. In 12th Annual IEEE/ACM International Symposium on Code Generation and Optimization, CGO 2014, Orlando, FL, USA, February 15--19, 2014, David R. Kaeli and Tipp Moseley (Eds.). ACM, 87. https://dl.acm.org/citation.cfm?id=2544151

[66]

Albert Mingkun Yang, Erik Österlund, and Tobias Wrigstad. 2020. Improving program locality in the GC using hotness. PLDI 2020: Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation, New York, NY, 301--313.

Digital Library

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cover image ACM Conferences

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

May 2022

2508 pages

ISBN:9781450392211

DOI:10.1145/3510003

General Chair:
Matthew B Dwyer
University of Virginia
,
Program Chairs:
Daniela Damian
University of Victoria, Canada
,
Andreas Zeller
CISPA, Germany

Copyright © 2022 ACM.

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Published: 05 July 2022

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https://doi.org/10.3390/software3010004
Murazvu GParkinson SKhan SLiu NAllen G(2024)A Survey on Factors Preventing the Adoption of Automated Software Testing: A Principal Component Analysis ApproachSoftware10.3390/software30100013:1(1-27)Online publication date: 2-Jan-2024
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Wang JWang YSong JCheng H(2023)IoV Vulnerability Classification Algorithm Based on Knowledge GraphElectronics10.3390/electronics1223474912:23(4749)Online publication date: 23-Nov-2023
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